Abstract
One of the most important steps in the process of conducting a systematic review or map is data extraction and the production of a database of coding, metadata and study data. There are many ways to structure these data, but to date, no guidelines or standards have been produced for the evidence synthesis community to support their production. Furthermore, there is little adoption of easily machine-readable, readily reusable and adaptable databases: these databases would be easier to translate into different formats by review authors, for example for tabulation, visualisation and analysis, and also by readers of the review/map. As a result, it is common for systematic review and map authors to produce bespoke, complex data structures that, although typically provided digitally, require considerable efforts to understand, verify and reuse. Here, we report on an analysis of systematic reviews and maps published by the Collaboration for Environmental Evidence, and discuss major issues that hamper machine readability and data reuse or verification. We highlight different justifications for the alternative data formats found: condensed databases; long databases; and wide databases. We describe these challenges in the context of data science principles that can support curation and publication of machine-readable, Open Data. We then go on to make recommendations to review and map authors on how to plan and structure their data, and we provide a suite of novel R-based functions to support efficient and reliable translation of databases between formats that are useful for presentation (condensed, human readable tables), filtering and visualisation (wide databases), and analysis (long databases). We hope that our recommendations for adoption of standard practices in database formatting, and the tools necessary to rapidly move between formats will provide a step-change in transparency and replicability of Open Data in evidence synthesis.
Highlights
Why databases are integral to evidence syntheses One of the most important steps in the process of conducting a systematic review or map is data extraction: locating and abstracting information and study findings from within each manuscript and entering these data into a designed database
In order to bridge this toolchain gap between best practices in scientific computing and evidence synthesists’ practice, we provide some examples of data structures and tools
Systematic review management tools (e.g. SysRev.com) have come a long way to reduce unnecessary errors, but there remains no consistent approach to data extraction across platforms that hampers Open Synthesis [7]
Summary
Why databases are integral to evidence syntheses One of the most important steps in the process of conducting a systematic review or map is data extraction: locating and abstracting information and study findings from within each manuscript and entering these data into a designed database. Various benefits to Open Data have been cited, including: increasing opportunities for reuse and further analysis (reducing research waste; [13]); facilitating real-time sharing and use of data [14]; increasing research visibility, discovery, impact and recognition [15]; facilitating research validity through replication and verification [16]; decreasing the risk of research fraud through transparency [17]; use of real research in educational materials [17]; facilitating collaborative research and reducing redundancy and research waste across siloed groups [18]; enabling public understanding [18]; increased potential to impact policy [19]; promotion of citizen science [20] These benefits are the same for Open Data in evidence synthesis (i.e. systematic reviews and maps). Systematic review management tools (e.g. SysRev.com) have come a long way to reduce unnecessary errors, but there remains no consistent approach to data extraction across platforms that hampers Open Synthesis [7]
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